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Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4

Andrei Pruteanu

This course will teach you how to create deep-learning algorithms for detecting and mitigating anomalies in data such as time series.

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This course will teach you how to create deep-learning algorithms for detecting and mitigating anomalies in data such as time series.

In this course, Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4, you’ll learn to spot specific patterns in large datasets that can be labelled as anomalies. First, you’ll explore how to precisely define anomalies in data. Next, you’ll discover detection algorithms. Finally, you’ll learn how to mitigate anomalous data. When you’re finished with this course, you’ll have the skills and knowledge of creating machine learning algorithms needed for dealing with various anomalies in data.

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What's inside

Syllabus

Course Overview
Introduction
Exploratory Data Analysis
Definition and Anomaly Types
Read more
Detection Algorithms
Mitigation Techniques

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explores methods which are highly relevant to data science and machine learning
Strengthens an existing foundation for intermediate learners
Taught by instructors recognized for their work in machine learning
Develops professional expertise in anomaly detection
Requires learners to come in with extensive background knowledge first

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 with these activities:
Review ‘Deep Learning with Python’ by Francois Chollet
Review the fundamentals of deep learning to strengthen your understanding of the concepts covered in the course.
View Deep Learning with Python, Second Edition on Amazon
Show steps
  • Read the first two chapters of the book.
  • Take notes on the key concepts and techniques.
  • Complete the practice exercises at the end of each chapter.
Follow Tensorflow Tutorials on Time Series Anomaly Detection
Supplement your learning by following guided tutorials, reinforcing your understanding of TensorFlow and anomaly detection techniques.
Browse courses on TensorFlow
Show steps
  • Visit the Tensorflow website and search for time series anomaly detection tutorials.
  • Select a tutorial that aligns with your skill level.
  • Follow the steps in the tutorial and implement the code.
  • Review the results and troubleshoot any issues.
Join a Study Group to Discuss Course Concepts
Collaborate with peers to exchange knowledge, clarify concepts, and enhance your understanding of the course material.
Browse courses on Anomaly Detection
Show steps
  • Reach out to classmates or fellow students.
  • Form a study group of 3-5 people.
  • Schedule regular meetings to discuss course concepts.
  • Prepare questions, share insights, and work through problems together.
  • Provide constructive feedback to each other.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend a Data Science Meetup on Anomaly Detection
Connect with professionals in the field and learn about industry applications of anomaly detection.
Browse courses on Anomaly Detection
Show steps
  • Search for data science meetups in your area.
  • Find a meetup that focuses on anomaly detection or related topics.
  • Attend the meetup and engage with speakers and attendees.
Build a Time Series Anomaly Detection Model
Apply the concepts learned in the course to a practical project, solidifying your understanding of anomaly detection techniques.
Browse courses on Time Series Analysis
Show steps
  • Choose a time series dataset.
  • Preprocess the data and identify anomalies.
  • Build and train a deep learning model for anomaly detection.
  • Evaluate the performance of the model.
Write a Blog Post on Anomaly Detection Techniques
Summarize and share your knowledge on anomaly detection techniques, reinforcing your understanding and helping others.
Browse courses on Data Analysis
Show steps
  • Identify key concepts and techniques related to anomaly detection.
  • Research and gather relevant information.
  • Write a draft of the blog post.
  • Review and edit the post.
  • Publish the blog post on a platform like Medium or LinkedIn.
Create a Course Summary Document
Synthesize your learning by creating a comprehensive summary of key concepts, techniques, and resources from the course.
Browse courses on Anomaly Detection
Show steps
  • Review lecture notes, readings, and other course materials.
  • Identify key concepts and organize them logically.
  • Summarize important techniques and algorithms.
  • Include relevant examples and illustrations.
  • Proofread and refine the document.

Career center

Learners who complete Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data scientists are experts at collecting, cleaning, analyzing, and presenting data. They use their skills to solve problems, predict trends, and develop new products and services. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical, statistical, and programming skills needed to be a successful data scientist. This course's focus on anomaly detection is particularly relevant, as data scientists often need to identify and address anomalies in their data.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
Machine learning engineers design, build, and maintain machine learning models. They use their skills to develop solutions to a variety of problems, including fraud detection, customer segmentation, and anomaly detection. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the technical skills needed to be a successful machine learning engineer. This course's focus on deep learning and TensorFlow is particularly relevant, as these are essential technologies in the field of machine learning.
See salaries and explore the career path for Machine Learning Engineer
Business Analyst
Business analysts help organizations improve their performance by analyzing data and identifying opportunities for improvement. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical and problem-solving skills needed to be a successful business analyst. This course's focus on anomaly detection is particularly relevant, as business analysts often need to identify and address anomalies in their data.
See salaries and explore the career path for Business Analyst
Operations Research Analyst
Operations research analysts use mathematical and analytical techniques to solve problems and improve decision-making. They work with a variety of industries, including healthcare, manufacturing, and finance. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical and problem-solving skills needed to be a successful operations research analyst. This course's focus on anomaly detection is particularly relevant, as operations research analysts often need to identify and address anomalies in their data.
See salaries and explore the career path for Operations Research Analyst
Statistician
Statisticians collect, analyze, and interpret data. They use their skills to solve problems, predict trends, and develop new products and services. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical and statistical skills needed to be a successful statistician. This course's focus on anomaly detection is particularly relevant, as statisticians often need to identify and address anomalies in their data.
See salaries and explore the career path for Statistician
Software Engineer
Software engineers design, develop, and maintain software applications. They work with a variety of technologies, including programming languages, databases, and operating systems. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the technical skills needed to be a successful software engineer. This course's focus on deep learning and TensorFlow is particularly relevant, as these are essential technologies in the field of software engineering.
See salaries and explore the career path for Software Engineer
Data Analyst
Data analysts discover, clean, and present data that can be used to analyze trends, monitor performance, and support decision-making. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical and technical skills needed to work with data in a meaningful way. This course's exploration of exploratory data analysis and anomaly detection algorithms can be particularly valuable.
See salaries and explore the career path for Data Analyst
Financial Analyst
Financial analysts help organizations make investment decisions by analyzing data and providing recommendations. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical and financial skills needed to be a successful financial analyst. This course's focus on anomaly detection is particularly relevant, as financial analysts often need to identify and address anomalies in their data.
See salaries and explore the career path for Financial Analyst
Risk Analyst
Risk analysts help organizations identify and manage risks. They use their skills to assess risks, develop mitigation plans, and monitor risks. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical and risk management skills needed to be a successful risk analyst. This course's focus on anomaly detection is particularly relevant, as risk analysts often need to identify and address anomalies in their data.
See salaries and explore the career path for Risk Analyst
Data Engineer
Data engineers build and maintain the infrastructure that supports data analysis. They work with a variety of technologies, including databases, data warehouses, and big data platforms. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the technical skills needed to be a successful data engineer. This course's focus on anomaly detection is particularly relevant, as data engineers often need to identify and address anomalies in their data.
See salaries and explore the career path for Data Engineer
Quantitative Analyst
Quantitative analysts use mathematical and statistical techniques to analyze data and make investment decisions. They work with a variety of financial institutions, including hedge funds, investment banks, and asset management companies. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical and quantitative skills needed to be a successful quantitative analyst. This course's focus on anomaly detection is particularly relevant, as quantitative analysts often need to identify and address anomalies in their data.
See salaries and explore the career path for Quantitative Analyst
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They work with a variety of industries, including insurance, healthcare, and finance. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical and risk management skills needed to be a successful actuary. This course's focus on anomaly detection is particularly relevant, as actuaries often need to identify and address anomalies in their data.
See salaries and explore the career path for Actuary
Data Science Manager
Data science managers lead teams of data scientists and oversee the development and implementation of data science projects. They work with a variety of industries, including healthcare, manufacturing, and finance. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the leadership and management skills needed to be a successful data science manager. This course's focus on anomaly detection is particularly relevant, as data science managers often need to identify and address anomalies in their data.
See salaries and explore the career path for Data Science Manager
Machine Learning Manager
Machine learning managers lead teams of machine learning engineers and oversee the development and implementation of machine learning projects. They work with a variety of industries, including healthcare, manufacturing, and finance. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the leadership and management skills needed to be a successful machine learning manager. This course's focus on anomaly detection is particularly relevant, as machine learning managers often need to identify and address anomalies in their data.
See salaries and explore the career path for Machine Learning Manager
Artificial Intelligence Manager
Artificial intelligence managers lead teams of artificial intelligence engineers and oversee the development and implementation of artificial intelligence projects. They work with a variety of industries, including healthcare, manufacturing, and finance. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the leadership and management skills needed to be a successful artificial intelligence manager. This course's focus on anomaly detection is particularly relevant, as artificial intelligence managers often need to identify and address anomalies in their data.
See salaries and explore the career path for Artificial Intelligence Manager

Reading list

We've selected seven books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4.
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
A comprehensive resource for machine learning with Scikit-Learn, Keras, and TensorFlow. Covers a wide range of machine learning algorithms and techniques, with a focus on practical applications.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Deep Learning with R, Second Edition
Save
A must-have reference for TensorFlow and Keras practitioners. Provides a comprehensive overview of deep learning models and architectures, along with practical guidance on building and training deep learning models.
Deep Learning with R, Second Edition
Paperback
$$$
Deep Learning with R, Second Edition
Kindle Edition
$$
Cover image
Hands-On Machine Learning with Scikit-Learn and...
Save
A visually appealing and intuitive introduction to deep learning. Provides a clear and accessible explanation of the fundamental concepts and algorithms of deep learning.
Hands-On Machine Learning with Scikit-Learn and...
Paperback
Check
Hands-On Machine Learning with Scikit-Learn and...
Kindle Edition
Check
Cover image
Deep Learning for Coders with fastai and PyTorch
Save
A practical guide to deep learning for coders. Provides a hands-on approach to building and training deep learning models using Fastai and PyTorch.
Deep Learning for Coders with Fastai and PyTorch:...
Paperback
$$
Deep Learning for Coders with fastai and PyTorch:...
Kindle Edition
$$
Cover image
Time Series Analysis and Its Applications
Save
A comprehensive textbook on time series analysis. Provides a rigorous treatment of the field, covering topics such as stationarity, seasonality, and forecasting methods.
Time Series Analysis and Its Applications
Paperback
Check
Time Series Analysis and Its Applications
Kindle Edition
Check
Cover image
Data Science from Scratch
Save
An excellent resource for beginners in data science. Covers the fundamental concepts and techniques of data science, including data cleaning, data manipulation, and data visualization.
Data Science from Scratch: First Principles with...
Paperback
$$
Data Science from Scratch: First Principles with...
Kindle Edition
$$
Data Science from Scratch: First Principles with...
Paperback
$$
Cover image
Time Series Analysis
Save
A classic work on time series analysis and forecasting. Provides a comprehensive overview of the field, covering topics such as stationarity, seasonality, and forecasting methods.
Time Series Analysis: Forecasting and Control...
Hardcover
$$$$
Time Series Analysis: Forecasting and Control...
Kindle Edition
$$$$

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Course image
Time
92 hours
Level
Advanced
Via
Pluralsight
Instructor
Andrei Pruteanu
Language
English
Good to know
Explores methods which are highly relevant to data science and machine learning
Strengthens an existing foundation for intermediate learners
Taught by instructors recognized for their work in machine learning
Develops professional expertise in anomaly detection
Requires learners to come in with extensive background knowledge first
Share and help others discover this course.
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